Text augmentation (TA) is a critical technique for text classification, especially in few-shot settings. This paper introduces a novel LLM-based TA method, TARDiS, to address challenges inherent in the generation and alignment stages of two-stage TA methods. For the generation stage, we propose two generation processes, SEG and CEG, incorporating multiple class-specific prompts to enhance diversity and separability. For the alignment stage, we introduce a class adaptation (CA) method to ensure that generated examples align with their target classes through verification and modification. Experimental results demonstrate TARDiS's effectiveness, outperforming state-of-the-art LLM-based TA methods in various few-shot text classification tasks. An in-depth analysis confirms the detailed behaviors at each stage.
@article{arxiv.2501.02739,
title = {TARDiS : Text Augmentation for Refining Diversity and Separability},
author = {Kyungmin Kim and SangHun Im and GiBaeg Kim and Heung-Seon Oh},
journal= {arXiv preprint arXiv:2501.02739},
year = {2025}
}